docs: def get_embeddings(self, rand=None, dtype='float32'):
          """
          Retrieves the embeddings for the vocabulary.
  
          :param rand: Random initialization function for out-of-vocabulary words. Defaults to `np.random.uniform(-0.1, 0.1, size=shape)`.
          :param dtype: Type of the matrix.
          :return: embeddings corresponding to the vocab instance.
  
          NOTE: this function will download potentially very large binary dumps the first time it is called.
          """
          rand = rand if rand else lambda shape: np.random.uniform(-0.1, 0.1, size=shape)
          embeddings = get_data_or_download('senna', 'embeddings.txt', self.embeddings_url)
          words = get_data_or_download('senna', 'words.lst', self.words_url)
  
          E = rand((len(self), self.n_dim)).astype(dtype)
  
          seen = []
          for word_emb in zip(self.gen_word_list(words), self.gen_embeddings(embeddings)):
              w, e = word_emb
              if w in self:
                  seen += [w]
                  E[self[w]] = e
          self.backfill_unk_emb(E, set(seen))
          return E